Prevalence of smoking habit and associated factors among students of Al- Andalus university of medical sciences

Background: smoking is the major cause of many chronic diseases and a growing public health problem in the world. - Aim: the aim of this study is to determine prevalence of smoking habit and associated factors among students of Al-Andalus university of medical science. - Methods: A cross sectional study was conducted from October academic year 2017 to March academic year 2018 on 300 students in Al- andalus Medical University. A systematic stratified sampling method was used. Data collected by self-administrated questionnaire. - Results: out of the 300 respondents, 166 students were smokers giving a prevalence rate of 55%.The prevalence of smokers were much higher in males than females (79.5% and 20.5%, respectively). 72.2% of students started smoking at the age of less than 20 years. There were a significance differences between faculties (P= 0.02) , which faculty of medicine reported high percentage. - Conclusions: This study directs the attention to the fact that problem of smoking among university students has important contributing personal and socio demographic factors. The study recommends integrating health awareness programmes about smoking hazards in the medical education curriculum.

Diffusion of Innovation and Generalized Rational Models

The purpose of this research is to develop and use two generalized Rational Models (GRM I, GRM II), each of which is a realizable mathematical model, not available with other models, and we will demonstrate its utility and applicability on a large scale, compared to other ( -shaped) models, and converging well.

Instant Electrical Load Forecasting By Using Artificial Neural Networks

In this research, We present a scientific advanced developed study and keeping up with new studies and technologies of very short-term electrical load forecasting and applying this study for electrical load forecasting of basic Syrian electrical power system by studying this prediction for next four hours according to the criterion applied in the Syrian Electricity Ministry with ten minutes intervals ,we call it "Instant electrical load forecasting".

Prediction of Monthly Reference Evapotranspiration in Homs Using Artificial Neural Network and Fuzzy Inference System

Accurate estimating and predicting of hydrological phenomena plays an influential role in the development and management of water resources, preparing of future plans according to different scenarios of climate changes. Evapotranspiration is one of the major meteorological components of the hydrologic cycle and from the most complex of them, and the accurate prediction of this parameter is very important for many water resources applications. So, this research goals to prediction of monthly reference evapotranspiration (ET0) at Homs meteostation, in the middle of Syrian Arab Republic, using Artificial Neural Networks (ANNs), and Fuzzy Inference System (FIS), depending on available climatic data, and comparision between the results of these models. The used data contained 347 monthly values of Air Temperature (T), Relative Humidity (RH), Wind Speed (WS) and Sunshine Hours (SS) (from October 1974 to December 2004). The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the proposed method by Food and Agriculture Organization of the United Nations (FAO) as the standard method for the estimation of ET0, and used as outputs of the models. The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) pridected successfully the monthly ET0 using climatic data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R), and showed that the using of the monthly index as an additional input, improves the accurate of prediction of the artificial neural networks models. Also, the results showed good ability of Fuzzy Inference Models (FIS) in predicting of monthly reference evapotranspiration. Sunshine hours are the most influential single parameter for ET0 prediction (R= 97.71%, RMSE = 18.08 mm/month) during the test period, sunshine hours and wind speed are the most influential optimal combination of two parameters (R= 98.55%, RMSE = 12.49 mm/month) during the test period. The results showed high reliability for each of the artificial neural networks and fuzzy inference system with a little preference for artificial neural networks which can add the monthly index in the input layer, and there for improve the presicion of predictions. This study recommends the using of artificial intelligence techniques in modeling of complex and nonlinear phenomena which related of water resources.

A Comparative Study between Artificial Neural Network Performance and Adaptive Neuro-fuzzy Inference Systems in Breast Cancer Diagnosis Depending On Structural Features

This research aims to produce a diagnosis system for breast cancer by using Neural Network depending on Back Propagation algorithm(BPNN) and Adaptive Neuro Fuzzy Inference System ‘ANFIS’, the both of studies was done using structural features of biopsies in “Wisconson Breast Cancer “data base. In the end a comparison was made between the two studies of malignant- benign classification of breast masses of breast cancer which has accuracy 95,95% with BPNN and 91.9% with ANFIS system, this results can be consider very important if they compared with researches depending on image features that obtained of various devises like mammography, magnetic resonance.

Prevalence of QX- Strain of Infectious Bronchitis in The Middle and Coastal Region of Syria

This study was conducted to investigate the incidence of infectious bronchitis virus (IBV) QX type in commercial broiler flocks in the Middle and Coastal Region of Syria.

Experimental and Analytical Study of absorbance phenomenon in unsaturated polyester – wood flour

In this study, we have investigated the water absorption behavior of unsaturated polyester /wood flour wastes composites materials. To achieve that, specimens were prepared by using compressing method with different ratio of polymer matrix with organic wastes produced from carpentry workshop (wood flour).Density of produced panels has been measured and the obtained results showed that there is an ability to produce hardindustrial wood panels. Practical experiments had been achieved to determine the percentages of water absorption. Absorption test was achieved on the cut specimens by immersing them in natural water (un-distilled) and measuring the gained weight of specimens and the resulted swelling to determine the final changes in the product. Through this study, we find that the absorbability has increased with the increment of organic filler ratio and the practices sizes increment. In addition, we also find that the absorption behavior follow Fickian law of diffusion in most specimens. We calculated the diffusion coefficient D and other parameters of diffusion process and we also plotted the associated plotsof the absorbability results. The obtained results showed that there is an ability to produce planes of industrial wood without any pretreatment of wood flour.

Prediction of Monthly Pan Evaporation in Homs Meteostation Using Artificial Neural Networks

Evaporation is a major meteorological component of the hydrologic cycle, and it plays an influential role in the development and management of water resources. The aim of this study is to predict of the monthly pan evaporation in Homs meteostation using Artificial Neural Networks (ANNs), which based on monthly air temperature and relative humidity data only as inputs, and monthly pan evaporation as output of the network. The network was trained and verified using a back-propagation algorithm with different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan evaporation with total correlation coefficient equals 96.786 % and root mean square error equals 24.52 mm/month for the total data set. This study recommends using the artificial neural networks approach to identify the most effective parameters to predict evaporation.

Assess Power System Voltage Stability Using Neural Networks

The contribution of our research include building an artificial neural network in MATLAB program environment and improvement of maximum loading point algorithm, to compute the most critical voltage stability margin, for on-line voltage stability assessment, and a method to approximate the most critical voltage stability margin accurately. a method to create a (ARTIFICIAL NEURAL NETWORK) approach.

Operational Electrical Load Forecasting By Using Artificial Neural Networks

In this paper, we presented a scientific methodicalness in very short term load forecasting depends on back propagation artificial neural networks, and we relied upon real data of Syrian electrical power system.